mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-05 13:12:53 +08:00
206 lines
8.4 KiB
Python
206 lines
8.4 KiB
Python
import os
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import torch
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import datasets
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import transformers
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from typing import Any, Dict, Optional, Tuple
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from transformers import HfArgumentParser, Seq2SeqTrainingArguments
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from transformers.trainer_utils import get_last_checkpoint
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from llmtuner.extras.logging import get_logger
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from llmtuner.extras.misc import parse_args
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from llmtuner.hparams import (
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ModelArguments,
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DataArguments,
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EvaluationArguments,
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FinetuningArguments,
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GeneratingArguments
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)
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logger = get_logger(__name__)
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_TRAIN_ARGS = [
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ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments
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]
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_TRAIN_CLS = Tuple[
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ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments
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]
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_INFER_ARGS = [
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ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
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]
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_INFER_CLS = Tuple[
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ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
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]
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_EVAL_ARGS = [
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ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments
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]
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_EVAL_CLS = Tuple[
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ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments
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]
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def _verify_model_args(model_args: "ModelArguments", finetuning_args: "FinetuningArguments") -> None:
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if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
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raise ValueError("Quantization is only compatible with the LoRA method.")
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if (
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model_args.checkpoint_dir is not None
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and len(model_args.checkpoint_dir) != 1
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and finetuning_args.finetuning_type != "lora"
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):
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raise ValueError("Multiple checkpoints are only available for LoRA tuning.")
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def parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
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parser = HfArgumentParser(_TRAIN_ARGS)
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return parse_args(parser, args)
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def parse_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
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parser = HfArgumentParser(_INFER_ARGS)
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return parse_args(parser, args)
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def parse_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
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parser = HfArgumentParser(_EVAL_ARGS)
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return parse_args(parser, args)
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def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
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model_args, data_args, training_args, finetuning_args, generating_args = parse_train_args(args)
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# Setup logging
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if training_args.should_log:
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# The default of training_args.log_level is passive, so we set log level at info here to have that default.
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transformers.utils.logging.set_verbosity_info()
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log_level = training_args.get_process_log_level()
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datasets.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# Check arguments
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data_args.init_for_training(training_args.seed)
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if finetuning_args.stage != "pt" and data_args.template is None:
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raise ValueError("Please specify which `template` to use.")
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if finetuning_args.stage != "sft" and training_args.predict_with_generate:
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raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
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if finetuning_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
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raise ValueError("Please enable `predict_with_generate` to save model predictions.")
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if finetuning_args.stage in ["rm", "ppo"] and training_args.load_best_model_at_end:
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raise ValueError("RM and PPO stages do not support `load_best_model_at_end`.")
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if finetuning_args.stage == "ppo" and not training_args.do_train:
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raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")
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if finetuning_args.stage in ["rm", "dpo"] and (not all([data_attr.ranking for data_attr in data_args.dataset_list])):
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raise ValueError("Please use ranked datasets for reward modeling or DPO training.")
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if finetuning_args.stage == "ppo" and model_args.shift_attn:
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raise ValueError("PPO training is incompatible with S^2-Attn.")
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if training_args.max_steps == -1 and data_args.streaming:
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raise ValueError("Please specify `max_steps` in streaming mode.")
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if training_args.do_train and training_args.predict_with_generate:
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raise ValueError("`predict_with_generate` cannot be set as True while training.")
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if training_args.do_train and finetuning_args.finetuning_type == "lora" and finetuning_args.lora_target is None:
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raise ValueError("Please specify `lora_target` in LoRA training.")
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_verify_model_args(model_args, finetuning_args)
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if training_args.do_train and model_args.quantization_bit is not None and (not finetuning_args.upcast_layernorm):
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logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
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if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
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logger.warning("We recommend enable mixed precision training.")
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if (not training_args.do_train) and model_args.quantization_bit is not None:
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logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
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if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
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logger.warning("Specify `ref_model` for computing rewards at evaluation.")
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# postprocess training_args
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if (
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training_args.local_rank != -1
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and training_args.ddp_find_unused_parameters is None
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and finetuning_args.finetuning_type == "lora"
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):
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logger.warning("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
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training_args_dict = training_args.to_dict()
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training_args_dict.update(dict(ddp_find_unused_parameters=False))
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training_args = Seq2SeqTrainingArguments(**training_args_dict)
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if (
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training_args.resume_from_checkpoint is None
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and training_args.do_train
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and os.path.isdir(training_args.output_dir)
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and not training_args.overwrite_output_dir
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):
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last_checkpoint = get_last_checkpoint(training_args.output_dir)
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if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
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raise ValueError("Output directory already exists and is not empty. Please set `overwrite_output_dir`.")
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if last_checkpoint is not None:
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training_args_dict = training_args.to_dict()
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training_args_dict.update(dict(resume_from_checkpoint=last_checkpoint))
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training_args = Seq2SeqTrainingArguments(**training_args_dict)
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logger.info("Resuming training from {}. Change `output_dir` or use `overwrite_output_dir` to avoid.".format(
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training_args.resume_from_checkpoint
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))
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if finetuning_args.stage in ["rm", "ppo"] and training_args.resume_from_checkpoint is not None:
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logger.warning("Add {} to `checkpoint_dir` to resume training from checkpoint.".format(
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training_args.resume_from_checkpoint
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))
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# postprocess model_args
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model_args.compute_dtype = (
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torch.bfloat16 if training_args.bf16 else (torch.float16 if training_args.fp16 else None)
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)
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model_args.model_max_length = data_args.cutoff_len
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# Log on each process the small summary:
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logger.info("Process rank: {}, device: {}, n_gpu: {}\n distributed training: {}, compute dtype: {}".format(
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training_args.local_rank, training_args.device, training_args.n_gpu,
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bool(training_args.local_rank != -1), str(model_args.compute_dtype)
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))
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logger.info(f"Training/evaluation parameters {training_args}")
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# Set seed before initializing model.
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transformers.set_seed(training_args.seed)
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return model_args, data_args, training_args, finetuning_args, generating_args
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def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
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model_args, data_args, finetuning_args, generating_args = parse_infer_args(args)
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if data_args.template is None:
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raise ValueError("Please specify which `template` to use.")
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_verify_model_args(model_args, finetuning_args)
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return model_args, data_args, finetuning_args, generating_args
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def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
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model_args, data_args, eval_args, finetuning_args = parse_eval_args(args)
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if data_args.template is None:
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raise ValueError("Please specify which `template` to use.")
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_verify_model_args(model_args, finetuning_args)
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transformers.set_seed(eval_args.seed)
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return model_args, data_args, eval_args, finetuning_args
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